Data Scientist CV: Example and Template
How to structure your CV as a Data Scientist for the Swiss job market - with measurable model results instead of lists of methods.
Currently 19 open Data Scientist positions in Switzerland, across 4 cantons, 3 of them from the last 7 days. pg advertises the most.
CV example — two-page template
This template is editable straight away — or upload your existing CV and it is carried into this layout automatically.
What qualification is expected of Data Scientists in Switzerland?
A quantitative university degree - MSc or PhD from an ETH, EPFL, university or university of applied sciences - is standard, complemented by a continuing education certificate.
Write your degree the way Swiss HR professionals recognise it: 'MSc ETH in Data Science', 'MSc EPFL in Computational Science', 'MSc in Statistics, University of Bern' or 'BSc FHNW Data Science'. If you come from physics, mathematics, econometrics, bioinformatics or business information technology, also state your quantitative specialisation and your master's thesis in one line with method and result. For foreign diplomas, add a note on recognition by Swissuniversities or the SBFI - this saves HR a follow-up question.
Continuing education from the Swiss higher education system carries more weight than a long list of online courses. Common and recognised are CAS or MAS programmes in Data Science, Machine Learning or Data Engineering at HSLU, ZHAW, FHNW, BFH or OST, as well as MAS programmes in Applied Information and Data Science. Add at most three to four certificates with an exam component, such as Azure DP-100, Databricks ML Professional, AWS ML Specialty or SAS certifications in the pharma and banking sectors.
Also mention what counts in your industry: in insurance and banking, knowledge of model validation according to FINMA circulars and actuarial fundamentals (SAV environment); in pharma, GxP and CDISC knowledge; in the public sector, experience with Federal Statistical Office (FSO) statistics and Open Government Data. For all roles: data protection is expert knowledge. A brief note on revDSG, GDPR touchpoints and anonymisation procedures shows that you build legally sound models.
How do you build compelling work experience and projects?
For each position, describe the data context, the model and the business impact in figures - not just the library used.
For each position, use two lines of context: team size, data volume, data platform and business domain. A sentence like 'Data & Analytics team of 11 people, 480'000 policies, Databricks Lakehouse on Azure' says more about your level than any self-assessment. This is followed by three to four bullet points, each with method and impact: 'Churn model (LightGBM, AUC 0.87) reduced cancellations by 18 percent and secured CHF 2.1 million in premium revenue'.
Show the entire value chain, because this is exactly where many applications fail. Demonstrate that you don't just write notebooks, but model data (dbt, star schema), orchestrate pipelines (Airflow, Azure Data Factory), deploy models (MLflow, containers, batch and online scoring) and monitor them (drift, data quality, retraining). Add your role in collaboration with data engineering, IT security and business units, as well as your way of working (Scrum, two-week sprints, code reviews).
Without several years of practice, replace work experience with verifiable projects: master's thesis, internship, working student role, Kaggle placement with rank and number of participants, or a public repository. Here too, phrase things measurably - dataset size, baseline, metric achieved, runtime. A link to GitHub or a short case study page is common in Switzerland and is actually clicked by technical hiring managers.
How long should the CV be, does it need a photo and salary information?
Two pages, photo optional but common, salary information only if requested in the job posting.
Stick to two pages; for a research career with publications, three pages plus a separate publication list are acceptable. The first page contains contact details, a short profile of three sentences, the two most recent positions and a compact skills block, structured by languages, ML methods, data platforms and visualisation. Avoid progress bars for competencies - instead write 'PySpark, 5 years in production' or 'SQL, window functions and query tuning'.
A professional portrait photo remains common in Switzerland and is usually expected, though it is optional. State your place of residence and region (for example 'Zurich, commutable to Zug/Aarau'), nationality or permit status (C permit, B permit, EU/EFTA), year of birth optional, and your earliest possible start date. For cross-border commuters, a note on the G cross-border permit helps. Save the document as a PDF with a descriptive filename and ensure ATS readability: single column, no text boxes, job title spelled out in full.
Salary expectations belong in the cover letter, not the CV, and only if the posting asks for them. As a guide: entry-level data scientists in the greater Zurich area often earn CHF 90'000 to CHF 110'000, with three to five years of experience CHF 110'000 to CHF 140'000, senior and lead roles above that; state a range and reference it to 13 monthly salaries. Finally, adapt your short profile and skills block to each posting - if a company is looking for NLP, they should find NLP projects in the first five lines.
Where Data Scientist are hired in Switzerland
How the 19 open positions are spread across the cantons.
Figures as a table
| Canton | postings |
|---|---|
| Genf | 7 |
| Zürich | 4 |
| Waadt | 2 |
| Freiburg | 1 |
Which languages the postings require
Of 17 postings that state a language — in brackets, those requiring professional level.
Figures as a table
| Language | postings |
|---|---|
| English | 17 (16) |
| French | 2 (2) |
| German | 1 (1) |
Who hires Data Scientist in Switzerland
Employers with the most open positions. Staffing agencies are excluded.
Figures as a table
| Employer | postings |
|---|---|
| pg | 3 |
| onrunning | 3 |
| Direktion der Justiz und des Innern Kanton Zürich | 1 |
| Agoda | 1 |
| Oracle | 1 |
| Experis Switzerland | 1 |
Full-time or part-time?
How the positions are advertised.
Figures as a table
| Workload | postings |
|---|---|
| Vollzeit / plein temps | 10 |
The CV in full
To read through and reuse.
Nadine Brunschwiler
Senior Data Scientist, MSc ETH in Data Science, CAS Machine Learning HSLU
Data Scientist with 8 years of experience in insurance, retail and banking, specialised in forecasting models, price optimisation and production MLOps pipelines on Azure and Databricks. I bring models from notebook into operation: 14 models in production, monitored with MLflow and automated drift monitoring. Collaboration with actuarial, compliance and IT security teams, including documentation according to revDSG and FINMA requirements for model validation.
What sets me apart
From prototype to production: I am responsible for the entire lifecycle: feature store, CI/CD with Azure DevOps, model registry in MLflow, drift alerting. Average time from proof of concept to go-live: 9 weeks instead of the previous 7 months.
Technical language for actuarial and business: I translate GLM, gradient boosting and survival models for underwriters and executive management, presenting results without formulas - instead with contribution margin, combined ratio and confidence intervals.
Compliance-ready models: Model documentation, bias tests and explainability (SHAP) according to revDSG Art. 21 (automated individual decisions) as well as internal model validation following the four-eyes principle - passed three external audits with no findings.
Cost-conscious cloud architecture: I optimise Spark clusters, partitioning and job scheduling: reduced Databricks costs from CHF 34'000 to CHF 19'000 per year, while handling 40 percent more workloads.
Key achievements
Churn model reduces attrition by 18 percent. Gradient boosting model (LightGBM, AUC 0.87) with prioritised contact lists for customer service; 18 percent fewer cancellations in the target group, securing CHF 2.1 million in annual premium revenue.
Dynamic price optimisation in retail. Elasticity models for 12'000 items across 84 stores, weekly price recommendations; gross margin increased by 2.4 percentage points, markdowns reduced by CHF 780'000.
Real-time fraud detection. Anomaly detection on payment flow (Isolation Forest plus rule engine), latency under 200 ms; false positive rate reduced from 6.2 to 1.9 percent, saving 220 hours of manual review per month.
Experience
Senior Data Scientist — Alpstein Versicherungen AG, Zürich, 03/2021 - present
Data & Analytics team of 11 people, claims and non-life business, Databricks Lakehouse on Azure.
- Developed churn and cross-selling models for 480'000 policies; AUC 0.87, 18 percent fewer cancellations in the treated cohort
- Claims forecasting (frequency/severity with Tweedie GLM and XGBoost) improved reserve estimation; forecast error MAPE reduced from 21 to 12 percent
- Introduced MLOps standard (MLflow, feature store, automated retraining): reduced time-to-production from 7 months to 9 weeks
- Provided technical leadership to 3 junior data scientists and 2 interns, raised code review rate to 100 percent of pull requests
Data Scientist — NovaRetail Schweiz AG, Dietikon, 08/2018 - 02/2021
84 stores and online shop, sales planning and pricing, Snowflake plus dbt.
- Demand forecasting at item-store level for 12'000 SKUs; improved forecast accuracy by 16 percent, reduced out-of-stock rate from 4.1 to 2.3 percent
- Brought price elasticity model into production: gross margin plus 2.4 percentage points, markdowns minus CHF 780'000 per year
- Implemented basket analysis and recommendation logic in the online shop; average order value plus CHF 7.40 (A/B test, n = 96'000, p < 0.01)
- Documented 42 dbt models with tests, reducing data quality errors in reports by 65 percent
Data Scientist / Data Analyst — Limmatwerk Analytics GmbH, Zürich, 09/2016 - 07/2018
Analytics consultancy with 25 employees, mandates in banking, energy and pharma.
- Managed 11 client projects ranging from CHF 40'000 to CHF 260'000 in volume as Analytics Lead, customer satisfaction 4.7 out of 5
- Developed fraud detection for payment transactions: false positives reduced from 6.2 to 1.9 percent, saving 220 review hours per month
- Automated reporting pipelines (Python, Airflow) replaced 3 manual Excel processes, saving 18 hours per week
- Built internal training format for SQL and Python, 34 participants across 6 courses
Education
Master of Science ETH, Data Science — ETH Zürich · 2016
Bachelor of Science, Business Information Technology — ZHAW School of Management and Law, Winterthur · 2014
CAS, Machine Learning and Data Engineering — Hochschule Luzern (HSLU) · 2020
Microsoft Certified: Azure Data Scientist Associate (DP-100), 2023 · Databricks Certified Machine Learning Professional, 2022 · AWS Certified Machine Learning - Specialty, 2021 · Professional Scrum Master I (PSM I), 2019